With machine learning you have the same kind of problems with an added factor of unpredictability. ML to make the recommendations more dynamic per user means p

With machine learning you have the same kind of problems with an added factor of unpredictability. ML to make the recommendations more dynamic per user means people are not seeing the same things and engagement tanks. ML to make the recommendations more global means a lot of people are going to see stuff they don't care about and they're going to have no idea why. It's usually this family of problems that ML adds an extra layer of opacity to the most. Then you add extra tabs to give the user more control, and now you've added a fresh complexity trade off. The sanest aggregators have simplistic recommendation algorithms and a bunch of janitorial work on top in the form of moderation/censorship. There must be something to this formula.